A method to alter standard errors from spatial autocorrelation using lavaan and ape
R
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
Readme_files/figure-markdown_strict
Boreality.txt
Readme.Rmd
Readme.md
lavSpatialCorrect.R
ndvi_se_corrected.R

Readme.md

Correcting Standard Errors in SEMs fit with Covariance Matrices and ML using Moran's I

This code addresses the problem of correcting sample sizes and standard errors in the presence of spatial autocorrelation in Structural Equation Models with with spatial structure in the autocorrelation of endogenous variables. Two file are included. The first, ndvi_se_corrected.R, walks through the steps to calculate corrections piece by piece. You can see a longer explanation at http://www.imachordata.com/?p=1714

The second is a function that impelements the correction for all endogenous variables, using Moran's I and an approximation of an effective sample size for large sample sizes.

For an example, consuder the Boreal Vegetation dataset from Zuur et al.'s Mixed Effects Models and Extensions in Ecology with R. The data shows vegetation NDVI from satellite data, as well as a number of other covariates - information on climate (days where the temperature passed some threshold, I believe), wetness, and species richness. And space. Here's what the data look like, for example:

# Boreality data from http://www.highstat.com/book2.htm
# Mixed Effects Models and Extensions in Ecology with R (2009). 
# Zuur, Ieno, Walker, Saveliev and Smith. Springer
boreal <- read.table("./Boreality.txt", header=T)

#For later
source("./lavSpatialCorrect.R")

#Let's look at the spatial structure
library(ggplot2)

qplot(x, y, data=boreal, size=Wet, color=NDVI) +
  theme_bw(base_size=18) + 
  scale_size_continuous("Index of Wetness", range=c(0,10)) + 
  scale_color_gradient("NDVI", low="lightgreen", high="darkgreen")

We can fit a model using lavaan where NDVI is affected by species richness (nTot), wetness (Wet), and climate (T61) and richness is itself also affected by climate.

library(lavaan)

## This is lavaan 0.5-17
## lavaan is BETA software! Please report any bugs.

# A simple model where NDVI is determined
# by nTot, temperature, and Wetness
# and nTot is related to temperature
borModel <- '
  NDVI ~ nTot + T61 + Wet 
  nTot ~ T61
'

#note meanstructure=T to obtain intercepts
borFit <- sem(borModel, data=boreal, meanstructure=T)

However, the residuals show autocorrelation. One way to see this is just to look at the spatial pattern of the signs of residuals.

# residuals are key for the analysis
borRes <- as.data.frame(residuals(borFit, "casewise"))

#raw visualization of NDVI residuals
qplot(x, y, data=boreal, color=borRes$NDVI, size=I(5)) +
  theme_bw(base_size=17) + 
  scale_color_gradient("NDVI Residual", low="blue", high="yellow")

#raw visualization of sign of residuals
qplot(x, y, data=boreal, color=borRes$NDVI>0, size=I(5)) +
  theme_bw(base_size=17) + 
  scale_color_manual("NDVI Residual >0", values=c("blue", "red"))

lavSpatialCorrect calculates Moran's I for the residuals of all endogenous variables, and then spatially corrects them via Moran's I. If they are spatially independent, the effective sample size = the true sample size.

lavSpatialCorrect(borFit, boreal$x, boreal$y)

## Loading required package: ape

## $Morans_I
## $Morans_I$NDVI
##     observed     expected          sd p.value    n.eff
## 1 0.08265236 -0.001879699 0.003985846       0 451.6189
## 
## $Morans_I$nTot
##     observed     expected          sd p.value    n.eff
## 1 0.03853411 -0.001879699 0.003998414       0 493.4468
## 
## 
## $parameters
## $parameters$NDVI
##             Parameter      Estimate    n.eff      Std.err   Z-value
## NDVI~nTot   NDVI~nTot -0.0003567484 451.6189 0.0001848868  -1.92955
## NDVI~T61     NDVI~T61 -0.0354776273 451.6189 0.0024493462 -14.48453
## NDVI~Wet     NDVI~Wet -4.2700526589 451.6189 0.1436405689 -29.72734
## NDVI~~NDVI NDVI~~NDVI  0.0017298286 451.6189 0.0001151150  15.02696
## NDVI~1         NDVI~1 10.8696158663 451.6189 0.7268790958  14.95382
##                  P(>|z|)
## NDVI~nTot   5.366259e-02
## NDVI~T61    1.517587e-47
## NDVI~Wet   3.404230e-194
## NDVI~~NDVI  4.889505e-51
## NDVI~1      1.470754e-50
## 
## $parameters$nTot
##             Parameter    Estimate    n.eff     Std.err   Z-value
## nTot~T61     nTot~T61    1.170661 493.4468   0.5674087  2.063171
## nTot~~nTot nTot~~nTot  112.051871 493.4468   7.1336853 15.707431
## nTot~1         nTot~1 -322.936937 493.4468 168.1495917 -1.920534
##                 P(>|z|)
## nTot~T61   3.909634e-02
## nTot~~nTot 1.345204e-55
## nTot~1     5.479054e-02